{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import xgboost as xgb\n",
    "import sys,random\n",
    "import pickle\n",
    "import os\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import modin.pandas as pd\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "if not os.path.exists('featurescore'):\n",
    "    os.mkdir('featurescore')\n",
    "if not os.path.exists('model'):\n",
    "    os.mkdir('model')\n",
    "if not os.path.exists('preds'):\n",
    "    os.mkdir('preds')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 59.5 s, sys: 2.42 s, total: 1min 1s\n",
      "Wall time: 1min 1s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "data_date = pd.read_csv('../../preprocess_data/train_x_date.csv').drop(columns=['id','loan_hour'])\n",
    "data_raw = pd.read_csv('../../preprocess_data_new/train_ax_nodup.csv',nrows=33465).drop(columns=['id','loan_dt','tag'])\n",
    "# data_raw = pd.read_csv('../../preprocess_data/train_x_33465.csv').drop(columns=['id','loan_dt','tag'])\n",
    "# data_null = pd.read_csv('../../preprocess_data/train_x_null.csv').drop(columns=['id'])\n",
    "data_null = pd.read_csv('../../preprocess_data_new/train_ax_row_null.csv',nrows=33465).drop(columns=['id'])\n",
    "data_tag = pd.read_csv('../../preprocess_data/train_x_33465.csv',usecols=['tag'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# data_raw_nan = data_raw.dropna(axis=1,how='all')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 2.56 s, sys: 68 ms, total: 2.63 s\n",
      "Wall time: 2.63 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "data = pd.concat([data_date,data_raw,data_null,data_tag],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 4 ms, sys: 0 ns, total: 4 ms\n",
      "Wall time: 4.29 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "data_label = pd.read_csv('../../preprocess_data/train_y_33465.csv',usecols=['label'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(33465, 6702)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "使用旧数据集 train集的维度为：  \n",
    "使用新数据集 train集的维度为：6702"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 交叉验证"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.PCA降维"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.decomposition import PCA\n",
    "pca = PCA(n_components=0.99).fit(data.fillna(0).values)\n",
    "data_pca = pca.transform(data.fillna(0).values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_pca.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "开始训练！\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split # 分割数据模块\n",
    "from sklearn.metrics import roc_curve\n",
    "from sklearn import metrics\n",
    "\n",
    "#1.分割数据\n",
    "\n",
    "# x的挑选\n",
    "# a.基于fscore\n",
    "# feature_score = pd.read_csv('./feature_score.csv')\n",
    "feature_names = data.columns\n",
    "# feature_names = [x for x in feature_names if x!='tag']\n",
    "x= data[feature_names].values\n",
    "# b.基于PCA\n",
    "# x=data_pca\n",
    "y=data_label.values\n",
    "X_train, X_test, y_train, y_test = train_test_split(x,y,test_size=0.3, random_state=3096)\n",
    "#2.建立模型\n",
    "params={\n",
    "    'booster':'gbtree',\n",
    "    'objective': 'binary:logistic',\n",
    "    'early_stopping_rounds':100,\n",
    "    'scale_pos_weight': float(len(data_label)-np.sum(data_label.values))/float(np.sum(data_label.values)),  # 负例样本除以正例样本\n",
    "    'eval_metric': 'auc',\n",
    "    'gamma':1,\n",
    "    'max_depth':6,\n",
    "    'lambda':1,\n",
    "    'subsample':0.9,\n",
    "    'colsample_bytree':0.9,\n",
    "    'min_child_weight':1, \n",
    "    'eta': 0.04,\n",
    "    'seed':2010,\n",
    "    'nthread':16\n",
    "        }\n",
    "#3.训练模型\n",
    "# a.基于fscore\n",
    "# dtrain = xgb.DMatrix(X_train, label=y_train, feature_names=feature_names)\n",
    "# b.基于PCA\n",
    "dtrain = xgb.DMatrix(X_train, label=y_train)\n",
    "# watchlist  = [(dtrain,'train')]\n",
    "print('开始训练！')\n",
    "model  = xgb.train(params,dtrain,num_boost_round=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "开始预测！\n",
      "AUC: 0.9397646211320003\n"
     ]
    }
   ],
   "source": [
    "# 4.预测结果\n",
    "print('开始预测！')\n",
    "# a.基于fscore\n",
    "# dtest = xgb.DMatrix(X_test, feature_names=feature_names)\n",
    "# b.基于PCA\n",
    "# dtest = xgb.DMatrix(X_test)\n",
    "# c.基于LDA\n",
    "dtest = xgb.DMatrix(X_test)\n",
    "y_pre = model.predict(dtest)\n",
    "auc = metrics.roc_auc_score(y_test, y_pre)\n",
    "print('AUC:',auc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "raw AUC: 0.8130921791239684  \n",
    "raw 去除nan列 AUC: 0.8125236195981279  \n",
    "raw 去除nan列 + 统计null AUC: 0.8263001992035767   \n",
    "nodup + null AUC: 0.8261527677046496  \n",
    "nodup + null + tag AUC:0.8296696198580618  \n",
    "nodup + null + tag (前4000维)AUC: 0.8251877022906782    \n",
    "nodup + null + tag (前5990维) AUC: 0.821991126741565  \n",
    "nodup + null + tag (前3000维) AUC: 0.8275583356303323  \n",
    "nodup + null + tag (前2000维) AUC: 0.8249837418081847"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "nodup + null + tag (PCA降至8维) AUC: 0.5830606257872517"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "feature_score  = model.get_fscore()\n",
    "feature_rank = sorted(feature_score,key=lambda x:x[1],reverse=True)\n",
    "import pickle\n",
    "pickle.dump(feature_rank,open('./cv_feat_rank.pkl','wb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import pickle\n",
    "# feature_rank = pickle.load(open('./cv_feat_rank.pkl','rb'))\n",
    "# feature_rank"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "14"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import gc\n",
    "del dtrain,dtest,model\n",
    "gc.collect()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 训练与预测"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 28.7 s, sys: 1.19 s, total: 29.9 s\n",
      "Wall time: 32.1 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "valid_date = pd.read_csv('../../preprocess_data/valid_date.csv').drop(columns=['id','loan_hour'])\n",
    "# valid_raw = pd.read_csv('../../preprocess_data/valid.csv').drop(columns=['id','loan_dt'])\n",
    "valid_raw = pd.read_csv('../../preprocess_data_new/valid_nodup.csv').drop(columns=['id','loan_dt'])\n",
    "valid_null = pd.read_csv('../../preprocess_data_new/valid_row_null.csv').drop(columns=['id'])\n",
    "valid_tag = pd.read_csv('../predict_tag/tag.csv',usecols=['tag'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 1.29 s, sys: 20 ms, total: 1.31 s\n",
      "Wall time: 1.31 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "valid = pd.concat([valid_date,valid_raw,valid_null,valid_tag],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "dtrain = xgb.DMatrix(data.values, data_label.values, feature_names=list(data.columns))\n",
    "dtest = xgb.DMatrix(valid.values, feature_names=list(valid.columns))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_id = pd.read_csv('../../preprocess_data_new/valid_date.csv',usecols=['id']).values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "def pipeline(dtrain,dtest,test_id,iteration,random_seed,gamma,max_depth,lambd,subsample,colsample_bytree,min_child_weight):\n",
    "    if max_depth==6:\n",
    "        num_boost_round = 500\n",
    "    elif max_depth==7:\n",
    "        num_boost_round = 400\n",
    "    elif max_depth==8:\n",
    "        num_boost_round = 300\n",
    "    \n",
    "    params={\n",
    "    'booster':'gbtree',\n",
    "    'objective': 'binary:logistic',\n",
    "    'early_stopping_rounds':100,\n",
    "    'scale_pos_weight': float(len(data_label)-np.sum(data_label.values))/float(np.sum(data_label.values)),  # 负例样本除以正例样本\n",
    "    'eval_metric': 'auc',\n",
    "    'gamma':gamma,\n",
    "    'max_depth':max_depth,\n",
    "    'lambda':lambd,\n",
    "    'subsample':subsample,\n",
    "    'colsample_bytree':colsample_bytree,\n",
    "    'min_child_weight':min_child_weight, \n",
    "    'eta': 0.04,\n",
    "    'seed':random_seed,\n",
    "    'nthread':16\n",
    "        }\n",
    "    watchlist  = [(dtrain,'train')]\n",
    "    model = xgb.train(params,dtrain,num_boost_round=num_boost_round,evals=watchlist)\n",
    "    model.save_model('./model/xgb{0}.model'.format(iteration))\n",
    "    \n",
    "    #predict test set\n",
    "    test_y = model.predict(dtest)\n",
    "    test_result = pd.DataFrame(test_id,columns=[\"id\"])\n",
    "    test_result['score'] = test_y\n",
    "    test_result.to_csv(\"./preds/xgb{0}.csv\".format(iteration),index=None,encoding='utf-8')\n",
    "    \n",
    "    #get feature score\n",
    "    feature_score = model.get_fscore()\n",
    "    feature_score = sorted(feature_score.items(), key=lambda x:x[1],reverse=True)\n",
    "    fs = []\n",
    "    for (key,value) in feature_score:\n",
    "        fs.append(\"{0},{1}\\n\".format(key,value))\n",
    "    \n",
    "    with open('./featurescore/feature_score_{0}.csv'.format(iteration),'w') as f:\n",
    "        f.writelines(\"feature,score\\n\")\n",
    "        f.writelines(fs)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0]\ttrain-auc:0.782696\n",
      "[1]\ttrain-auc:0.812435\n",
      "[2]\ttrain-auc:0.823735\n",
      "[3]\ttrain-auc:0.833812\n",
      "[4]\ttrain-auc:0.843099\n",
      "[5]\ttrain-auc:0.848643\n",
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      "[7]\ttrain-auc:0.856744\n",
      "[8]\ttrain-auc:0.85972\n",
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      "[10]\ttrain-auc:0.861093\n",
      "[11]\ttrain-auc:0.863894\n",
      "[12]\ttrain-auc:0.865728\n",
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      "[14]\ttrain-auc:0.868753\n",
      "[15]\ttrain-auc:0.870028\n",
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      "[17]\ttrain-auc:0.873765\n",
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      "[499]\ttrain-auc:0.996161\n",
      "CPU times: user 1d 4h 31s, sys: 2min 31s, total: 1d 4h 3min 2s\n",
      "Wall time: 1h 48min 50s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "random_seed = list(range(1000,2000,20))\n",
    "gamma = [i/1000.0 for i in range(100,200,2)]\n",
    "max_depth = [6,7,8]\n",
    "lambd = list(range(200,400,2))\n",
    "subsample = [i/1000.0 for i in range(600,700,2)]\n",
    "colsample_bytree = [i/1000.0 for i in range(250,350,2)]\n",
    "min_child_weight = [i/1000.0 for i in range(200,300,2)]\n",
    "random.shuffle(random_seed)\n",
    "random.shuffle(gamma)\n",
    "random.shuffle(max_depth)\n",
    "random.shuffle(lambd)\n",
    "random.shuffle(subsample)\n",
    "random.shuffle(colsample_bytree)\n",
    "random.shuffle(min_child_weight)\n",
    "\n",
    "with open('params.pkl','wb') as f:\n",
    "    pickle.dump((random_seed,gamma,max_depth,lambd,subsample,colsample_bytree,min_child_weight),f)\n",
    "\n",
    "#to reproduce my result, uncomment following lines\n",
    "\"\"\"\n",
    "with open('params_for_reproducing.pkl','rb') as f:\n",
    "    random_seed,gamma,max_depth,lambd,subsample,colsample_bytree,min_child_weight = pickle.load(f)    \n",
    "\"\"\"\n",
    "\n",
    "for i in range(36):\n",
    "    pipeline(dtrain,dtest,test_id,i,random_seed[i],gamma[i],max_depth[i%3],lambd[i],subsample[i],colsample_bytree[i],min_child_weight[i])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "raw 去除nan列 + 统计null AUC: 0.82751113123698  \n",
    "nodup + null + tag: 0.82803008109167"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(33465, 6702)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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